色母粒需求量预测数据

应用场景：色母粒需求量预测数据聚焦于预测色母粒市场的未来需求趋势，为公司及外部相关方提供了关键的决策依据，具有重要的应用价值。具体体现在以下方面: 1.精准预测不同规格、颜色、功能色母粒的未来需求体量，可科学制定生产计划，合理安排生产批次与产能分配，避免盲目生产导致的库存积压或库存短缺；同时为安全库存设定提供数据依据，保障生产连续性与订单交付及时性。 2.向原材料供应商共享合理的需求预测，帮助其提前规划产能、备料生产，保障原材料稳定供应，减少因供需错配导致的供应链中断风险。为下游塑料制品企业提供需求趋势参考，帮助客户提前规划采购计划、优化自身生产安排。算法规则简要说明：1. 数据采集。采集公司近1年（自数据统计月份往前1年）色母粒的销售数据，包括统计时间、产品名称、订单日期、购方名称、产品规格型号、订单数量（吨）、订单金额（人民币元）。 2. 数据预处理。对采集的数据进行清洗，去除重复记录，处理缺失值。 3. 数据加工与分析 （1）计算历史需求量：对于色母粒产品，使用“透视表数据 + VLOOKUP 函数”批量生成结果，分别计算出自数据统计日期往前的365天、90天和30天的总需求量。（2）建立需求量预测模型：色母粒产品的未来30天需求量预测值=[(过去365天总需求量÷365×a)+(过去90天的总需求量÷90×b)+(过去30天的总需求量÷30×c)]×30×k；其中，系数a=0.5，b=0.3，c=0.2，调整因子k=1.1。系数a、b、c反映数值对未来30天需求量预测的影响程度，由于算法更注重长期需求趋势的影响，因此a被赋予了最高的权重。调整因子k基于市场增长预期进行修正。

Application Scenario: The masterbatch demand forecasting dataset focuses on predicting future demand trends in the masterbatch market, providing critical decision-making basis for the company and external stakeholders, with significant application value, which is reflected in the following aspects:
1. Accurately forecasting the future demand volume of masterbatches with different specifications, colors and functions, which enables scientific formulation of production plans, reasonable arrangement of production batches and capacity allocation, avoiding overproduction leading to inventory overstock or stock shortage; meanwhile, it provides data support for setting safety stock, ensuring production continuity and timely order delivery.
2. Sharing reasonable demand forecasts with raw material suppliers to help them pre-plan production capacity and prepare raw materials in advance, ensuring stable supply of raw materials and reducing the risk of supply chain disruption caused by supply-demand mismatch. Providing demand trend references for downstream plastic product enterprises, helping customers plan procurement plans in advance and optimize their own production arrangements.

Brief Description of Algorithm Rules:
1. Data Collection. Collect the company's masterbatch sales data over the past 1 year (1 year prior to the data statistics month), including statistical time, product name, order date, buyer name, product specification/model, order quantity (ton), order amount (RMB yuan).
2. Data Preprocessing. Clean the collected data, remove duplicate records and handle missing values.
3. Data Processing and Analysis
(1) Calculate historical demand: For masterbatch products, use pivot table data + VLOOKUP function to generate batch results, and calculate the total demand over the past 365 days, 90 days and 30 days prior to the data statistics date respectively.
(2) Establish demand forecasting model: The 30-day future demand forecast value for masterbatch products is calculated as: [(Total demand over the past 365 days ÷ 365 × a) + (Total demand over the past 90 days ÷ 90 × b) + (Total demand over the past 30 days ÷ 30 × c)] × 30 × k; where the coefficients a=0.5, b=0.3, c=0.2, and the adjustment factor k=1.1. The coefficients a, b and c reflect the degree of impact on the 30-day future demand forecast. As the algorithm prioritizes the impact of long-term demand trends, a is assigned the highest weight. The adjustment factor k is revised based on market growth expectations.

搜集汇总

背景与挑战

背景概述

该数据集是浙江常山县和众新材料有限公司登记的色母粒需求量预测数据，包含1186条记录，每日更新，数据结构涵盖历史订单和未来30天需求预测字段。其特点在于采用基于历史销售数据的加权算法进行预测，应用场景包括优化生产计划、库存管理和供应链协调，为企业决策提供数据支持。